Sensing events affecting liquid flow in a liquid distribution system

ABSTRACT

By monitoring pressure transients in a liquid within a liquid distribution system using only a single sensor, events such as the opening and closing of valves at specific fixtures are readily detected. The sensor, which can readily be coupled to a faucet bib, transmits an output signal to a computing device. Each such event can be identified by the device based by comparing characteristic features of the pressure transient waveform with previously observed characteristic features for events in the system. These characteristic features, which can include the varying pressure, derivative, and real Cepstrum of the pressure transient waveform, can be used to select a specific fixture where a valve open or close event has occurred. Flow to each fixture and leaks in the system can also be determined from the pressure transient signal. A second sensor disposed at a point disparate from the first sensor provides further event information.

CROSS-REFERENCE

This application is a continuation of U.S. patent application Ser. No.15/010,177, filed Jan. 29, 2016, which is a continuation of U.S. patentapplication Ser. No. 13/888,917, filed May 7, 2013, now U.S. Pat. No.9,250,105, issued on Feb. 2, 2016, which is a continuation of U.S.patent application Ser. No. 12/483,041, filed Jun. 11, 2009, now U.S.Pat. No. 8,457,908, issued on Jun. 4, 2013, the disclosures of each ofwhich are incorporated herein by reference in their entirety

BACKGROUND

Water is essential to many home activities (e.g., washing, cleaning,cooking, drinking, gardening). In 2008, it was estimated by theEnvironmental Protection Agency (EPA) that 36 states will face seriouswater shortages in the next five years. Furthermore, in 2001, theAmerican Water Works Association indicated that only a 15% reduction inwater usage across US households would save an estimated 2.7 billiongallons/day and more than $2 billion/year. Adding to the problem is amore recent estimate by the EPA that more than 1 trillion gallons ofwater leak from the water systems in U.S. homes each year, whichaccounts for about 10% of the average home's water usage. The leaks canbe in worn faucet and toilet valves, as well as leaks in the water linesinstalled in living structures. Most consumers have no mechanism toaccurately measure their household water usage other than totalconsumption indicated on a monthly (or bi-monthly) water bill, which arebased on periodic water meter readings. Further, the leaks occurring inhousehold water systems often go undetected, since they are not evidentto residents in a home. In order to better conserve water and stopleaks, it will be necessary to provide residents with informationrelated to the water consumed with each type of water consumptionactivity, from washing a load of clothes in a washer, to taking a showeror flushing a toilet.

Previous work that was directed toward monitoring home water usagecreated an approach that has several drawbacks. For example, thisearlier approach used microphones pressed against an exterior ofspecific water pipes in a residence, including a cold water inlet, a hotwater inlet, and a waste water exit, to demonstrate recognition ofseveral important activities based on patterns of water use, such as theseries of fill cycles associated with a dishwasher. This earliertechnique was unable to reliably differentiate among multiple instancesof water usage by similar fixtures (e.g., the opening or closing ofvalves at each of multiple sinks or the flushing of multiple toiletswithin a home), could not reliably identify concurrent activities (e.g.,a toilet flush while a person is showering), and did not attempt toestimate the volume of water being used by the water system during waterconsuming activities. There are also significant difficulties in usingthe audio-based sensors due to ambient noise (e.g., noise produced by anair conditioning unit that is installed in close proximity to a sensorplaced on a home's hot water heater). In addition, this prior approachdid not enable leaks to be detected at specific fixtures.

In several industrial applications, such as irrigation systems, sensorsproviding high-granularity flow rate monitoring have been used, butthese prior art approaches are either prohibitively expensive forresidential use (e.g., from about $2,000 to about $8,000 for a singleultrasonic or laser Doppler velocimetry sensor) or require aprofessional installation by a plumber of multiple inline flow sensors.An inline flow sensor is installed for each fixture of interest bycutting into existing pipes. It has also been shown in a laboratoryenvironment that accelerometers mounted on the exterior of water pipesproduce a signal having a strong deterministic relationship to waterflow rate, but this effect is highly sensitive to pipe diameter,material, and configuration. Others have proposed using a home'sexisting aggregate water flow meter together with a network ofaccelerometers on pipes to infer flow rates throughout a home. However,all of these prior art approaches require placement of multiple sensorsalong or in water pipe pathways that are uniquely associated with eachfixture of interest (i.e., they are distributed direct sensing methodsthat cannot use a single sensor to monitor all of the fixtures in astructure water system).

It is therefore evident that it would be desirable to employ a bettermethod and system for monitoring water flow to each of a plurality ofdifferent fixtures in a residence or multi-living unit structure that islow in cost and easily installed without using a plumber. Such a systemand method should enable water usage or volumetric flow occurring ateach fixture to be readily determined. In addition, it would also bedesirable to employ such a system and method to detect water leakage atspecific fixtures or points in a water system of a structure, so that alocation of at least certain types of the leaks can be identified, tofacilitate correcting the condition that has caused the leak.

SUMMARY

This application specifically incorporates by reference the disclosuresand drawings of each patent application and issued patent identifiedabove as a related application.

An exemplary novel method has thus been developed as described below,for monitoring a flow of a liquid in a distribution system within astructure. As used herein, the term “structure” is intended to encompassnot only living structures such as houses, multi-unit living quarters(such as duplexes), condominiums, townhouses, apartments, hotels,motels, etc., but also should be understood to include any facility thatincludes a system of pipes or conduits for distribution of liquids, suchas a refinery, a chemical manufacturing facility, and a brewery, to namea few examples without any intended or implied limitation. Thisexemplary method includes the steps of monitoring a liquid pressure at afirst point in the distribution system, and in response thereto,producing an output signal indicative of pressure in the distributionsystem. Liquid-related events occurring in the distribution system arethen detected based upon changes in pressure, e.g., transient pressurewaveforms, indicated by the output signal. Further, a specific type of aliquid-related event that has been detected from among a plurality ofdifferent types of events is identified, by comparing characteristics ofthe output signal with determinative criteria associated with theplurality of different types of events.

A plurality of different valves are typically coupled to thedistribution system. Accordingly, the step of detecting liquid-relatedevents can include employing the output signal for detecting a change instate of one or more of the valves, i.e., a valve opening more orclosing more. The valve that is identified can be associated with aspecific fixture from among a plurality of different fixtures that arecoupled to the distribution system, so that the specific fixture is thusidentified by detecting the valve opening or closing.

The method can further include the step of determining whether the valveassociated with the specific fixture has changed state by opening moreor closing more.

Some distribution systems may includes a reservoir (e.g., a toilet tank)with a valve that opens automatically if a level of the liquid in thereservoir drops below a predefined level. If so, the method can includethe step of detecting a leak from the reservoir by identifyingcharacteristics of a pressure transient waveform that are indicative ofa cycle in which the valve controlling a flow of the liquid into thereservoir opens and closes as required to refill the reservoir, toreplace liquid that has leaked from the reservoir.

As another function, the method can include the step of automaticallydetermining a volumetric flow rate in the distribution system as afunction of both the output signal and a predefined flow resistance forthe distribution system. If the distribution system includes a pluralityof valves disposed at different points, the method can comprise thesteps of empirically measuring the volumetric flow rate at each of aplurality of different points in the distribution system that are atvarying distances from an inlet for the distribution system; and,determining the predefined flow resistance for the distribution systemat each of the plurality of different points, based upon a change inpressure indicated by the output signal while the volumetric flow rateis being measured. The predefined flow resistance can then be estimatedfor other points where liquid usage can occur in the distributionsystem, based upon the predefined flow resistance measured at theplurality of different points.

In some applications, the liquid distribution system can include aninline liquid volumetric flow detector, e.g., a water meter. In thiscase, the method can further include the steps of using the inlineliquid volumetric flow detector to successively determine a volumetricflow rate at each of a plurality of different points in the distributionsystem. The volumetric flow rate is measured as a valve at the point isopened for a period of time and then closed. The predefined flowresistance for the distribution system is then determined at each of theplurality of different points, based upon the volumetric flow ratemeasured while the valve at that point was open. A relatively low-flowleak in the distribution system can be detected by using the liquidvolumetric flow detector to detect a flow of the liquid in thedistribution system for an extended period of time during which none ofthe valves in the distribution systems were determined to have beenopen. Any flow that is measured must thus result from the slow leak,since none of the liquid should be passing through nominally closedvalves.

The step of identifying a specific type of event that has been detectedcan comprise the steps of determining a predefined transient pressurewave signature for each fixture that is coupled to the distributionsystem, and storing or otherwise saving the predefined transientpressure wave signatures. A transient pressure wave signature indicatedby the output signal can then be compared to the predefined transientpressure wave signatures that were stored or saved, and a specificfixture where liquid flow has changed can be determined by identifyingthe fixture having the predefined transient pressure wave signature thatmost closely matches the transient pressure wave signature indicated bythe output signal, and based upon a location of the specific fixture inthe distribution system.

The step of identifying a specific type of liquid-related event that hasbeen detected can comprise the step of segmenting the output signal toisolate discrete events, based on pressure changes in the distributionsystem. Each discrete event that is detected can then be classified aseither a valve open or a valve close event. Also, each valve open orvalve close event can further be classified according to a specificfixture that generated it.

The step of segmenting can include the steps of filtering the outputsignal to produce a smoothed output signal, and determining a derivativeof the smoothed output signal. The smoothed output signal and itsderivative can then be analyzed in a sliding window to detect abeginning of a valve event based upon at least one condition. Thepossible conditions include those in which the derivative of thesmoothed output signal exceeds a predefined first threshold relative tostatic pressure in the distribution system, or in which the differencebetween a maximum pressure value and a minimum pressure value in thesliding window exceeds a predefined second threshold relative to thestatic pressure in the distribution system. The derivative of thesmoothed output signal can further be analyzed to detect an end of avalve event based upon a change in a sign of the derivative and amagnitude of a change in the derivative. The step of classifying eachdiscrete liquid-related event that is detected as either a valve open ora valve close event can be based on an occurrence of a conditionselected from the group of conditions, including: (a) a magnitude of adifference in the smoothed pressure at the beginning and the end of avalve event exceeding a third predefined threshold relative to thestatic pressure in the distribution system, wherein a decrease in thesmoothed pressure between the beginning and the end of the valve eventindicates a valve open event, and an increase in the smoothed pressurebetween the beginning and the end of the valve event indicates a valveclose event; or, (b) based on an average value of the derivative of thesmoothed pressure between the beginning of the valve event and a firstextreme of the derivative, wherein a positive average value of thederivative indicates a valve open event, and a negative average value ofthe derivative indicates a valve close event.

The method can include the step of associating valve open and valveclose events with specific fixtures using a template-based classifier.In this case, a template having a maximum correlation with thecharacteristics of the output signal is chosen and identifies thefixture for which an event has been detected. The choice is made afterfiltering potential templates that can be employed for the classifieraccording to a plurality of complementary distance metrics. Thesemetrics can include a matched filter distance metric, a matchedderivative filter distance metric, a matched real Cepstrum filterdistance metric, and a mean squared error filter distance metric. Themethod can further include the step of determining thresholds used tocarry out the step of filtering the potential templates, based on thecomplementary distance metrics provided in training data. If templatescorresponding to a plurality of different fixtures pass all of thefilters, a filter can be chosen from among the possible filters basedupon a single distance metric that performs best on training data forthe fixtures. The chosen filter can then be used in identifying thefixture for which an event has been detected.

The method can optionally include the step of monitoring liquid pressureat a second point in the distribution system, producing another outputsignal. The second point is spaced apart from the first point. Theliquid related events occurring in the distribution system can then bedetected based in part upon a time difference between the output signalat the first point and the output signal at the second point. Also, thespecific type of liquid-related event that has been detected can beselected from among the plurality of different types of events, based inpart upon the time difference.

Another option is to apply a transient pressure pulse to the liquid inthe distribution system (for example, by reverse biasing the pressuresensor), and detecting a pressure pulse waveform corresponding to areflection of the transient pressure pulse in the distribution system.Based upon characteristics of the pressure pulse waveform, one or moreof a path of the transient pressure pulse and the pressure pulsewaveform through the distribution system, an indication of liquid flowin the distribution system, and/or a state of one or more of the valvesin the distribution system can be determined.

Another aspect of the disclosure and claims is directed to a mediumincluding machine readable and executable instructions for carrying outa plurality of functions employed in monitoring a flow of a liquid in adistribution system within a structure when the machine readable andexecutable instructions are executed by a processor. These functions aregenerally consistent with the steps of the exemplary method discussedabove.

Still another aspect is directed to an exemplary apparatus formonitoring a flow of a liquid in a distribution system within astructure. The apparatus includes a pressure sensor that is adapted toconnect to a distribution system to sense a pressure in the distributionsystem and to then produce an analog signal indicative of the pressure.As used herein, the term “pressure sensor” is intended to be broadlyinterpreted to include any sensor that responds to liquid pressurephenomena in a pipe or conduit and may include without any implied orintended limitation, a sensor such as a piezoresistive sensor, a straingauge or other sensor that detects a mechanical deflection of adiaphragm, a micoelectromechanical system (MEMS) sensor, an opticalfiber interferometry sensor, a capacitive sensor (e.g., responding tochanges in a dielectric distance caused by pressure), an acousticssensor, and a vibration sensor (e.g., an accelerometer that responds topressure waveforms). A connector is provided and is sized for couplingthe pressure sensor to a fixture (such as a faucet bib) in a structure.An analog-to-digital converter is used for converting the analog signalfrom the pressure sensor to a digital signal. A micro controller iscoupled to the analog-to-digital converter to receive the digital signaland controls acquisition of the digital signal and processes the digitalsignal to produce an output signal that is used for detecting eventsoccurring in a distribution system, based upon changes in pressureindicated by the output signal. The output signal is used foridentifying a specific type of event from among a plurality of differenttypes of events. A communication link can be included for coupling theoutput signal to a computing device for further processing of the outputsignal.

Yet another aspect of the disclosure and claims that follow is directedto an exemplary system for monitoring a flow of a liquid in adistribution system within a structure. The system includes componentsgenerally consistent with those of the apparatus noted above, and alsoincludes a computing device. The computing device includes a memory thatstores machine executable instructions, and a processor that is coupledto the memory for executing the machine executable instructions.Execution of these machine instructions causes the processor to carryout a plurality of functions when the pressure sensor is connected to adistribution system. The functions are generally consistent with thesteps of the method discussed above.

This Summary has been provided to introduce a few concepts in asimplified form that are further described in detail below in theDescription. However, this Summary is not intended to identify key oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

DRAWINGS

Various aspects and attendant advantages of one or more exemplaryembodiments and modifications thereto will become more readilyappreciated as the same becomes better understood by reference to thefollowing detailed description, when taken in conjunction with theaccompanying drawings, wherein:

FIG. 1 is an exemplary schematic diagram of a basic water system in atwo-bedroom, two-bath residential structure, showing how the presentnovel approach can be installed at a single point, such as an externalhose bib faucet, to monitor water usage during different activities atfixtures in the structure, and to detect leaks that may occur in thewater system;

FIG. 2A is an exemplary graph showing a characteristic pressure (psi)vs. time (sec.) response detected using the present novel approachduring a valve open event, which is identified as the opening of akitchen faucet in a residential structure;

FIG. 2B is an exemplary graph showing a characteristic pressure (psi)vs. time (sec.) response detected using the present novel approachduring a valve close event, which is identified as the closing of thekitchen faucet that was opened in FIG. 2A;

FIG. 3 is a functional block diagram of a pressure sensor and controllerthat is used in one exemplary embodiment of the present novel approach,in which a Bluetooth radio is employed to transmit an output signalindicative of pressure to a computing device for further processing andclassification;

FIG. 4 is a graph summarizing data relating to nine residentialstructures in which the present novel approach was tested;

FIG. 5 are three graphs illustrating exemplary valve open and closepressure waves (pressure vs. time) respectively for a faucet, a toilet,and a tub, wherein a valve at the respective fixture was opened,remained open for an interval of time, and was then closed;

FIG. 6 is an exemplary table illustrating the percentage of fixturevalve open and fixture valve close events that were correctly identifiedin tests of the present novel approach in the nine residentialstructures of FIG. 4;

FIG. 7 is an exemplary table illustrating a different view of theresults of FIG. 6, wherein the percentage fixture valve open and closeevents correctly identified is illustrated for each type of fixture inthe test residential structures;

FIG. 8 is a table illustrating error data for flow rate determined foropen valves in four of the test residential structures indicated in FIG.4, using the present novel approach;

FIG. 9 is an exemplary graph showing average error in flow rate vs. thenumber of samples, for open valves in four of the test residentialstructures of FIG. 4;

FIG. 10 is an exemplary graph of pressure vs. time for a plurality ofoverlapping events occurring in a water system, wherein valves for ashower, a toilet, and a faucet are open over overlapping periods oftime, illustrating that the present novel approach is able to detecteach event and each fixture at which the event occurred;

FIG. 11 is a logic flow diagram illustrating exemplary steps that can beused in the present approach for detecting fixture/valve events;

FIG. 12 is a logic flow diagram illustrating exemplary steps that can beused for classifying valve events in accord with the present novelapproach;

FIG. 13A illustrates the raw output signals from two pressure sensorsdisposed at different points on a water system of a structure,illustrating the time delay between the output signals for a commonfixture event;

FIG. 13B illustrates the waveforms resulting from passing each of thetwo raw output signals of FIG. 13A through a 13 Hz low pass filter,clearly showing the time shift between the two waveforms due to thedifferent signal propagation paths to each pressure sensor from thefixture;

FIG. 14A indicates an active pressure signal that is used as a probesignal, wherein the active pressure signal is generated by a pressuretransducer and introduced into a water system of a structure, so thatthe active pressure signal propagates through the pipes;

FIG. 14B is a reflected pressure signal received from the water systempiping, a short time after the active pressure signal of FIG. 14A ended;and

FIG. 15 is an exemplary functional block diagram of a generallyconventional computing device, such as a personal computer, which isusable for processing the output signal from the pressure sensor andcontroller of the present novel system.

DESCRIPTION Figures and Disclosed Embodiments Are Not Limiting

Exemplary embodiments are illustrated in referenced Figures of thedrawings. It is intended that the embodiments and Figures disclosedherein are to be considered illustrative rather than restrictive. Nolimitation on the scope of the technology and of the claims that followis to be imputed to the examples shown in the drawings and discussedherein.

Exemplary System for Monitoring Water Usage

Most modem residences are connected to a public water supply or to aprivate well that provides water under pressure to the inlet of a watersystem in the residence. Public utilities rely on gravity and pumpingstations to distribute water through mains at a sufficient waterpressure to meet the requirements for water flow in each home or othertype of structure supplied water by the utility. Residences areconnected to a water main by a smaller service line, and a water meteris typically disposed at or near this connection. A backflow valve nearthe water meter prevents water from the structure flowing back into themain. Homes with private wells use a pump to draw the water out of theground and into a small captive air pressure tank within the home, whereit is stored under pressure, so that the pump does not need to runcontinually when a valve in the water system is opened.

FIG. 1 depicts a typical residential water system 20 for a two-bathroomstructure. Cold water enters through a service line 22 that is coupledto the water supply mains (or a private well), typically at 50-100pounds per square inch (psi) depending on such factors as the elevationof the home and its proximity to a reservoir or pumping station (orother factors if a private well is the source of the water). Many homeshave a pressure regulator 24 adjacent to a water meter 26 that protectsthe home from transients (or pressure spikes) that may propagate fromthe main and also reduces the incoming water pressure to a level safefor household fixtures and appliances.

Downstream of the regulator, there are two basic layouts found intypical residential piping, series plumbed and branched. Almost allmulti-fixture homes have a combination of these two layouts. A coldwater supply pipe 42 branches to the individual water fixtures (e.g., tosupply water to toilets, sinks, and showers) and also supplies coldwater to the supply inlet of a water heater 36. A traditional waterheater heats water in an insulated tank using electric resistanceelements, or a gas-fired burner (neither shown). When hot water is used,the pressure from the cold water supply line continuously forces the hotwater from the hot water tank through a hot water line 44 as the tankrefills with cold water. Every hot water tank has a pressure reliefvalve (not shown) to prevent a possible explosion due to excessiveoverheating and the resultant steam pressure, as well as a drain valve40 (important for maintenance, since water heaters should be drained atleast once a year to flush mineral deposits and increase operatingefficiency). Many homes also have a captive air thermal expansion tank38 connected near the cold supply inlet of the water heater if thesystem includes a backflow valve at the water meter and is thus a“closed system.” Thermal expansion tank 38 accommodates the thermalexpansion of the cold water that is being heated within the water heaterafter hot water is drawn from the hot water tank. Instead of a hot watertank that retains heated water until needed, some structures usetankless water heaters that provide hot water on demand by rapidlyheating the cold water as it passes through a heat exchanger, using heatsupplied by electric resistance elements or gas burners. Both types ofdevices for heating water create a connection between the cold and hotlines of a water system, and the pressure fluctuations monitored in thepresent approach are propagated through both types of water heaters forboth the hot water and cold water portions of a water system.

In this example, a pressure sensor 30 is threaded onto an exteriorfaucet bib 32. The valve on this faucet is opened so that the pressuresensor is able to respond to the pressure of the water system in thestructure, producing a corresponding signal that is processed andtransmitted as an output signal to a computing device, as described ingreater detail below.

Coupled to the water system in a first bathroom of the structure are afirst toilet 46, a first bathroom sink with a cold water valve 48 a anda hot water valve 48 b, and a bath tub 50 having both cold and hot watervalves (neither shown). A kitchen includes a kitchen sink with a coldwater valve 52 a and a hot water valve 52 b, and a dishwasher 54 (havinghot and cold water electro-mechanical solenoid valves neither shown). Ina second bathroom are a shower 56 (with hot and cold water valves—notshown), a second sink with a cold water valve 58 a and a hot water valve58 b, and a second toilet 60. The structure further includes a clotheswashing machine 62, which also includes electro-mechanical solenoidvalves (not shown) controlling both hot and cold water flow from thewater system.

Identifying Water Fixtures

The water system forms a closed loop pressure system, with water beingheld at a stable pressure throughout the piping when no water is flowingin the water system. Structures with a pressure regulator will have asubstantially stable pressure unless the supply pressure drops below theregulator's set point. Structures without a pressure regulator mayexperience occasional minor changes in water pressure depending onneighborhood water demands on the main supply line, which are detectedas fluctuations in the pressure of water in the water system of astructure.

When a valve is opened or closed (whether it a bathroom or kitchenfaucet or an electro-mechanical solenoid valve in a dishwasher orclothes washing machine), a pressure change occurs, and a transientpressure wave impulse is generated in the water system (as shownrespectively in graphs 100 and 102 in FIGS. 2A and 2B). Transientpressure is a wave phenomenon that results from a rapid change of watervelocity in a pipeline (similar to electrical transients on a powerline). The transient pressure wave occurring when a valve is rapidlyopened or closed is often referred to as a surge or water hammer and cansometimes create a loud hammering or audible noise as the pressureshockwave travels through pipes. The magnitude of the transient pressuresurge is both independent of and much greater than the operatingpressure. The transient pressure pulse can be either positive ornegative, depending on the positive or negative rate of change ofpressure (i.e., whether a valve is being opened or closed in the watersystem). Appliances such as dishwashers or clothes washers control theirelectromechanical solenoid valves so that they change state rapidly andthus often create the most pronounced water hammer. In contrast, afaucet valve that is opened or closed rather slowly produces less of awater hammer pulse.

An abrupt change in flow can create dangerously high transients thatexceed the safe operating pressure limits for residential pipes. Thermalexpansion tank 38 (FIG. 1) offers some, but not complete, dampening ofthese transients. In some water systems, air-filled standpipes areinstalled adjacent to the input lines to a wash machine or dish washerto provide local dampening of the transients. The change in state ofmost valves manifests as a water hammer impulse that is harmless, butwhich can be detected by a pressure sensor installed on the watersystem. Water hammer waveforms typically last several seconds, as thetransient pressure wave oscillates back and forth through the pipes.Using the present approach, the water hammer effect can be detectedanywhere within the water system (even with dampeners installed), thusenabling single-point sensing of effects throughout the water system.

The present approach relies on the fact that a unique pressure transientor water hammer signature sensed for a particular fixture depends on thevalve type and its location in the structure's water system. The abilityof the present approach to detect a location of an event provides greatdiscriminative power, making it possible to distinguish between twofixtures of the same model (e.g., between events occurring at two of thesame toilets in the house) and even between two valves in the samefixture (e.g., between the hot and cold water valves in a sink fixture)because their pressure wave impulses traverse different paths throughthe pipe infrastructure of the water system before reaching the pressuresensor. The magnitude of the pressure drop and resulting shockwave aredependent on a relative location of the pressure sensor to the source ofthe event, but the shape of the signature does not change. As discussedbelow, it is also contemplated that a plurality of pressure sensorsmight be installed at disparate points in the water system, so that atime difference between the transient pressure waveforms being detectedby the pressure sensors can provide additional information useful toidentify an event and the location of the fixture with which the eventis associated.

Estimating Flow

Changes in pressure and the rate of a pressure transient onset enablesaccurate detection of valve open and valve close events. Pressure canalso be used to measure flow rate in the water system, which isanalogous to an electrical circuit, where knowing the resistance (i.e.,pipe restrictions, bends, etc., which cause flow resistance) and thechange in voltage (i.e., pressure) enables a determination of theelectrical current (i.e., flow rate).

Flow rate is related to pressure change via Poiseuille's Law (alsoreferred to as the Hagen-Poiseuille equation), which states that thevolumetric flow rate of fluid in a pipe Q is dependent on the radius ofthe pipe r, the length of the pipe L, the viscosity of the fluid μ andthe pressure drop ΔP:

$\begin{matrix}{Q = {\frac{\Delta \; P\; \pi \; r^{4}}{8\mu \; L}.}} & (1)\end{matrix}$

Eq. (1) can be simplified by the fluid resistance formulation, whichstates that the resistance to flow is proportional to the drop inpressure divided by the volumetric flow rate.

$\begin{matrix}{R_{f} = {\frac{\Delta \; P}{Q} \equiv {\frac{8\mu \; L}{\pi \; r^{4}}.}}} & (2)\end{matrix}$

Thus, it is possible to use fluid resistance to abstract some of thevariable complexity from Poiseuille's Law, resulting in the simpleformula:

$\begin{matrix}{Q = {\frac{\Delta \; P}{R_{f}}.}} & (3)\end{matrix}$

The present novel approach measures the change in pressure ΔP when avalve is opened or closed. In order to compute Q, it is necessary toestimate the remaining unknown, R_(f). In this case, R_(f) is bounded bytwo factors: water viscosity and pipe length, L. Water viscosity caneasily be calculated as a function of the water temperature and based onthe fact that the internal diameter of most residential pipes are either¼″ or ⅜″. Thus, L, the length of the pipe, is the main unknown and willchange depending on the water fixture being used, since each path fromthe inlet of a water system, to each different fixture in the structure,is typically different.

These equations are not comprehensive. For example, they do not accountfor variations in the smoothness of the inner pipe surface, the numberof bends, valves, or constrictions in the pipes, nor pipe orientation(e.g., effects caused by the force of gravity and changes in barometricpressure). However, these effects can be treated as negligible forresidential pipe networks. The estimate for R_(f) can be simplified foreach residence by sampling the flow rate at strategic locations (so asto vary the distance from the water system inlet to the structure), andbased on a few measurements of resistance to flow at different valves,provide a reasonably accurate estimate for the resistance for theremaining valves in the structure.

Exemplary Pressure Monitor Design

As shown in FIG. 3, an exemplary embodiment of a pressure monitor 110includes a customized stainless steel pressure sensor 112, a 16-bitanalog-to-digital converter (ADC) 114, a microcontroller 116, and aBluetooth wireless radio 120. The Bluetooth wireless radio transmits anoutput signal 122 (conveyed as a Bluetooth radio signal) that isindicative of pressure in the water system sensed by pressure sensor112. (Alternatively, other types of wireless signals such as IEEE 802.11(WiFi), or a wired communication link (such as an Ethernet or USB line)may instead be used to convey the output signal to a computing devicefor further processing and storage.) Microcontroller 116 provides agating signal to close a field effect transistor switch 118 (or otherelectronic switch) to control the sampling of pressure by pressuresensor 112. A regulated power supply 124 provides direct current (DC)power to energize the entire pressure monitor. The computing device canbe a separate computer or can instead be incorporated into the pressuremonitor housing. Also, the output signal can be stored on the pressuremonitor using a memory. For example, a universal serial bus (USB) memorychip or other type of removable storage memory chip could be employed tostore the output signal for subsequent processing when the memory ismoved to a computing device. As a further alternative, the memory mightbe interrogated periodically to move the stored output signal data to acomputing device or another memory for subsequent processing.

Two different embodiments of the pressure sensor were employed,including one with a pressure range of 0-50 psi and the other with apressure range 0-100 psi. The higher dynamic range is useful formonitoring the water pressure in structures with a high supply pressureor when a pressure regulator is not included in the water system for thestructure. The pressure sensor used in this exemplary design was aP1600™ series manufactured by Pace Scientific, having a built-in ¼″ NPTmale connector, which was fitted with a ¾″ brass adaptor and sealedusing Teflon™ tape. This adaptor enabled the sensor to be easilythreaded onto any standard water faucet bib, such as those to which agarden hose might be connected. The pressure sensor has an operatingtemperature range of −40° F. to 257° F., and a pressure response time ofless than 0.5 milliseconds. The theoretical maximum sampling rate istherefore about 2 kHz, but 1 kHz should be more than sufficient fordetecting transients and a reasonable rate for the data transfer to acomputing device for processing. As noted above, many other types ofsensors that are responsive to pressure phenomena might be used in thepressure monitor instead of the Pace Scientific pressure sensor.

The pressure sensor's output is ratiometric to a 5 VDC supply voltage(i.e., the output voltage is a ratio relative to the supply voltage, sothat small changes in the supply voltage do not affect the level oraccuracy of the output signal). A 16-bit Texas Instruments ADS8344™ ADCand an AVR microcontroller were used in this exemplary pressure monitor,providing a resolution of approximately 0.001 psi for the 0-50 psipressure sensor and about 0.002 psi for the 0-100 psi pressure sensor.The Bluetooth radio is a Class 1, implementing a serial port profile.This exemplary pressure monitor embodiment was able to reliably sampleand stream output signal pressure data over the Bluetooth channel to aconventional personal computer (PC) (like that shown in FIG. 15) at adata rate of about 1 kHz. A 5 V low-drop power regulator chip was usedin regulated power supply 124 to regulate the DC voltage from a single 9V battery to provide power for the pressure monitor components. However,it is contemplated that a battery providing a different voltage, or adifferent type of battery, or an alternating current (AC) line sourcepower supply or some other power source might instead be used to providethe DC power to energize the components of pressure monitor 110.

Pressure sensor 112 has a mechanical shock rating of over 100 g, makingit insensitive to damage due to pipe vibration occasionally caused bysome water hammer events. Although the pressure sensor is calibrated andtested for linearity at the factory where it is made, the output of theentire pressure monitoring module was tested by applying known pressuresto the pressure sensor. Ten samples were taken with the pressure sensorconnected to a pressure-regulated water compressor providing anaccurately known water pressure. All measurements were well within thepressure sensor's tolerance of ±0.25% at 25° C. The entire unit isweatherproof and can be installed in damp locations. A current exemplaryimplementation of the pressure sensor coupling does not offer apass-through capability (i.e., does not enable the water system fixturewhere the pressure sensor is connected, to also be connected to a hoseor other coupling), but this modification can clearly be implemented byone of ordinary skill, e.g., by using a “T” or a “Y” fitting with theappropriate threaded ends.

Data Collection During Tests in Nine Residential Structures

In order to validate this novel approach, the exemplary pressure sensormodule, and the algorithms used to process the output signal from thepressure sensor module, labeled data were collected in nine residentialstructures, HI-H9, located in three cities. The residential structureswere of varying, style, age, and diversity of water systems, as shown ina table 130 in FIG. 4.

For each residential structure, the baseline static water pressure wasfirst measured, and the appropriate pressure sensor (i.e., the 0-50 psior the 0-100 psi range pressure sensor) was then installed on anavailable water hose bib, utility sink faucet, or water heater drainvalve. Each collection session was conducted by a pair of researchers.One person recorded the sensed pressure signatures on a laptop while theother activated the water fixtures in the structure. The pressuresignatures were recorded using a graphical logging tool, which alsoprovided real-time feedback of the pressure data via a scrollingtime-series line graph. Five trials were conducted for each valve oneach fixture (e.g., five trials for a hot water valve, and five trialsfor a cold water valve). For each trial, a valve was opened completelyfor at least five seconds and then closed.

For four of the nine residential structures (HI, H4, H5, and H7), flowrate information was also collected for kitchen and bathroom sink faucetand shower faucet fixtures. In addition to logging sensed pressure, thetime required to fill a calibrated container to a volume of one gallonwas measured (this method is preferred by water utilities for accuratelymeasuring flow rate). This step was repeated for five trials for eachvalve. The in-home data collection process yielded a total of 689fixture trials and 155 flow rate trials across 76 fixtures.

Overview of the Analysis of Fixture Event Identification

After the data were collected, a three-step approach was employed toexamine the feasibility of identifying individual fixture eventsaccording to unique transient pressure waves that propagated to thepressure sensor from each fixture where the event occurred. Recallingthat each valve event corresponds to a pressure transient signal when avalve is either opened or closed, each individual valve event was firstsegmented from the data stream, and its beginning and end wereidentified to enable further analysis. Next, each valve event wasclassified as either a valve open or a valve close event. Finally, thevalve event was classified according to the specific fixture thatgenerated it.

Initially, only events that occurred in isolation were identified. Theanalysis of compound (overlapping) events is discussed below.

Valve Event Segmentation

Before analyzing the characteristics of a valve event, the event mustfirst be segmented (i.e., isolated) from the pressure sensor outputsignal. Segmentation must be effective for many different types ofevents, and so, it is important to consider only those features of theoutput signal from the pressure sensor that are likely to be mosttypical of all valve events. The approach that was used is illustratedin graphs 140, 142, and 144 in FIG. 5 (and is also illustrated in graphs100 and 102 in FIGS. 2A and 2B). In an exemplary method, the raw outputsignal is smoothed using low pass, linear phase finite impulse responsefilters (e.g., a 13 Hz low pass filter and a 1 Hz low pass filter). Thesmoothed output signal from the 13 Hz low pass filter and a derivativeof the smoothed output signal from the 1 Hz low pass filter are thenanalyzed in a sliding window of 1000 samples (corresponding to onesecond of sensed pressure).

The beginning of a valve event corresponds to one of two conditions. Themost common is when the derivative of the smoothed pressure sensoroutput signal exceeds a specified threshold relative to static pressure,indicating a rapid change (for example, a derivative approximately equalto 2 psi/sec may be required for a residential structure water systemhaving 45 psi static pressure, scaled by the actual static pressure ofthe structure water system). A less common second condition is detectedwhen the difference between maximum and minimum values in the slidingwindow exceeds a threshold relative to the static pressure, indicating aslow but substantial change (for example, approximately 1 psi differencemay be required for a residential structure having a 45 psi staticpressure, scaled by the actual static pressure). After the beginning ofa valve pressure event is detected via either method, the next change inthe sign of the derivative represents the extreme of this valve eventrelative to the preceding static pressure (which may be either a maximumor a minimum).

The end of a segmented valve event can then be detected as a first pointat which an extreme of a fluctuation (i.e., a change in a sign of thederivative) is less than a predefined percentage (e.g., 5%) of themagnitude of the first extreme following the beginning of the event. Itis also possible for an event to end with a rapid increase in themagnitude of a fluctuation, which corresponds to the occurrence of acompound (or overlapping) event, as discussed below in greater detail.Applying this method to the data collected in a residential structureyielded appropriate segmentations of 100% of the valve events from theirsurrounding pressure output signal data stream.

Classifying Valve Open and Valve Close Events

After segmenting each valve event, the valve event is classified aseither a valve open or a valve close event. A valve open eventcorresponds to a valve opening more, while a valve close eventcorresponds to a valve closing more. Valve can open fully from a fullyclosed state, or can close fully from a fully open state, or can simplyopen more or close more than previously was the case. A classifier isapplied that first considers the difference in the smoothed pressure atthe beginning and the end of the segmented event. If the magnitude ofthis difference exceeds a threshold (for example, 2 psi for aresidential structure having a 45 psi static pressure, scaled by theactual static pressure), the event can be immediately classified (apressure decrease corresponds to a valve open and a pressure increase toa valve close event). Otherwise, the event is classified according tothe average value of the derivative between its beginning and its firstextreme. A valve open event creates an initial pressure decrease (apositive average derivative), while valve close events create an initialpressure increase (a negative average derivative). Applying this methodto the segmented valve events from the data that were collected from theresidential structures resulted in a 100% correct classification ofvalve open and valve close events.

Fixture Classification

Valve open and valve close events can be associated with specificfixtures in a structure using a template-based classifier. Whenclassifying an unknown event, the potential templates are first filteredaccording to four complementary distance metrics.

A first distance metric that is used is a matched filter, which is verycommon in signal detection theory. A matched filter is an optimaldetection mechanism in the presence of additive white noise. Its primarylimitation is that the pressure transient signals that are to bedifferentiated are not orthogonal. Making these signals orthogonal wouldrequire specific knowledge of the source of each event, which is exactlythe information that needs to be inferred.

The second distance metric is a matched derivative filter that isincluded because the derivatives of the events always resembleexponentially decreasing sinusoids. It is therefore reasonable toconclude that the derivatives are more orthogonal than the originalpressure signals, and that this matched derivative filter might providevalue distinct from the simple matched filter.

The third distance metric is based on the matched real Cepstrum filter,which is the inverse Fourier transform of the natural log of themagnitude of an event's Fourier transform. This metric attempts toapproximate the original version of a signal that has been run throughan unknown filter Gust as the valve event that is being classified hasbeen transformed by propagation of the pressure transient signal throughan unknown path in a structure's water pipes). The approach has definitelimitations, but it can be shown that the lower coefficients of theCepstrum result largely from the transfer function (an event'spropagation through a structure's pipes) and the higher coefficientslargely from the source (the original valve open/close event). Theprimary interest is in the transfer function (in part because it allowsdifferentiating among multiple instances of identical fixtures in ahome), and so the Cepstrum is truncated to include only the lowercoefficients. The resulting space is highly orthogonalized, yielding athird effective and complementary matched filter.

Finally, the fourth distance metric is a simple mean squared error(i.e., a Euclidean distance), which is computed by truncating the longerof two events detected in the water system, based on the pressure sensoroutput signal.

Similarity thresholds that are used to filter potential templates basedon these distance metrics can be learned from training data (i.e., thisstep provides for filtering templates whose similarity to the unknownevent are less than a minimum within-class similarity that has beenobserved in the training data). If no template passes all four filtersin regard to an event that has been detected, the unknown event is notclassified. In this case, an application might, for example, ignore theevent, prompt a person to label an unrecognized fixture, or determine ifthe unrecognized event indicates the presence of a leak. If templatescorresponding to multiple different fixtures pass all four of thedistance metric filters, a nearest-neighbor classifier defined by thesingle distance metric that performs best on the training data is chosenfrom among these templates. The single distance metric for the nearestneighbor classifier is chosen based upon the area under a within-classvs. out-of-class receiver operating characteristic (ROC) curve.

Fixture Classification Evaluation

Fixture classification is evaluated using an experimental designselected to demonstrate the robustness of learned model parametersacross the multiple residential structures in the collected test data.Specifically, a cross-validation experiment was conducted that relatesthe data according to the specific residential structure in which thedata were collected. There were nine trials in the cross-validation,with each trial using data from one residential structure as the testdata and data from the other eight residential structures as trainingdata. After learning model parameters from the test data (i.e., the foursimilarity filter thresholds and the choice of the distance metric forthe final nearest-neighbor classifier), each event in the testresidential structure was tested using a “leave-one-out method.” Eachtest residential structure event can then be classified using the otherevents as templates, together with the learned model parameters from thetraining data.

FIG. 6 presents the results of this evaluation in a table 150. Theaccuracy of fixture-level identification of valve open and valve closeevents within each home (and thus each test fold of thecross-validation), as well as the aggregate 95.6% accuracy offixture-level classification. FIG. 7 includes a table 160 that presentsa different view on the same data, showing the accuracy of fixture-levelclassification for different types of fixtures across all of theresidential structures. The overall fixture-level classification acrossall structures is well above 90%, including a number of cases where theclassification accuracy is 100%. Of particular note is the ability ofthe present novel approach to reliably distinguish among valves openingand closing at different sinks within a residential structure. The testdataset contains only a few instances of clothes washer or dishwateruse, in part due to time constraints during the test data collection andin part because it has been shown by others in the prior art that thesefixtures can be easily recognized by their highly-structured cycles ofwater usage (which can also be combined with the present novelapproach). However, the present novel approach is both independent ofthe number of fill cycles, which is important, for example, if adishwasher is sometimes run with an extra pre-rinse or with other cyclevariations, and enables recognition of the appliance that is in use assoon as a valve on any of these appliances is opened and they first usewater (in contrast to being able to recognize the appliance only aftertheir structured pattern of fill cycles becomes apparent over time).

Analysis of Flow Estimation

As discussed above, the volumetric flow rate Q is proportional to thechange in pressure ΔP divided by a resistance variable R_(f).

$Q = \frac{\Delta \; P}{R_{f}}$

The change in pressure ΔP is calculated automatically by measuring thedifference between the pressure at the onset of a detected valve openevent, and the stabilized pressure at the end of the segmented valveopen transient pressure wave impulse. The resistance variable R_(f)cannot be directly measured, but it can be empirically determined bycapturing ground truth flow rate information together with thecorresponding change in pressure for each valve in a structure. Thefollowing discussion considers two scenarios with regarding to learningR_(f). In the first scenario, it is assumed that a single calibration offlow is done for every valve of interest in a structure. In the secondscenario, an attempt is made to use information from the calibration ofonly some of the valves in the structure, to estimate R_(f) at the othervalves that have not been calibrated.

Individually Calibrated Valves

It is not unreasonable to imagine that the process of installing asystem like that discussed herein might include a single calibration foreach fixture in a structure. In this first scenario, by performing thisempirical determination, each valve in the home can be labeled with aknown R_(f) value that can subsequently be used with a sensed pressurechange in the water system, ΔP, to estimate water flow at the valve whenit is open.

The accuracy of the flow estimation that might be obtained in thisscenario was examined using a cross-validation experiment to analyze thefive calibrated container trial datasets collected for each of thefaucet and shower fixtures in residential structures HI, H4, H5, and H7(as discussed above). Each trial in the cross-validation used a singlecalibrated container test to infer a resistance variable R_(f) for avalve of the fixture. The inferred value of R_(f) was then used toestimate flow in the other four trials according to the measured changein pressure ΔP when the valve was opened. The difference was notedbetween these estimated flow rates (based on the inferred value ofresistance, R_(f)), and their corresponding actual flow rates (obtainedthrough the calibrated container trials). The results of this experimentare shown in a table 170, in FIG. 8.

Three of four residential structures tested (HI, H4, H5) had error ratesbelow 8% (or approximately 0.16 GPM), which is comparable to the 10%error rates found in empirical studies of traditional utility-suppliedwater meters. The fourth residential structure (H7), however, had anerror rate above 20%, which is believed to be due to the installationlocation of the sensor. Whereas the first three structures had thepressure sensor installed on an exterior water bib, in H7, the pressuresensor was installed on the hot water tank drain valve. Connecting thepressure sensor to the hot water tank drain valve results in thepressure sensor responding to both the supply water main pressure andthe head pressure of the water in the tank. As discussed above, thesimple pressure model employed in the present novel approach currentlyassumes a straight pipe and does not consider head pressure. It islikely that this situation requires a different model of R_(f). Itappears that cold water valves in H7 were particularly affected by thiserror source. Indeed, removing H7's four cold water valves from theanalysis dramatically improves the average error to 0.15 GPM (SD=0.18),or 4.5% (SD=3.8%).

Estimating R_(f) for Uncalibrated Valves

In the second scenario, where only a subset of the valves in a structurehave been directly calibrated to determine resistance R_(f), it seemsreasonable to attempt to build a model of fluid resistance for theentire structure from that calibration for the subset of valves. The keyidea is that although the pathway to each valve in the structure isunique, those paths also share a substantial amount of spatial overlapin the length and overall layout of the piping. For example, the toiletand sink in a particular bathroom typically share the same branch in thewater system and the path lengths are about the same.

To examine this approach, the calibrated container trials data wereseparated into two datasets, including a model and a test dataset. Themodel was initially populated by a single randomly selected trial, whichwas then used to infer a baseline R_(f) value. This R_(f) value was usedto calculate a flow estimate for each trial in the test dataset,comparing each to the corresponding actual flow. Next, a second randomtrial was added to the model (and removed it from the test dataset);then, the model was used to create a linear regression (Q=R_(f)*ΔP+b,where b is a constant). The linear regression equation was used tocalculate flow estimates for the remaining trials in the test set, andthe process was repeated until all trials had been sampled. To avoid aparticularly fortunate or unfortunate random sampling, this process wasrepeated five times for each residential structure, and the results wereaveraged. A graph 180 in FIG. 9 presents results 182, 184, 186, and 188for residential structures HI, H4, H5, and H7, respectively (note thatthe results for the cold water valves were excluded from the curve forresidential structure H7, as discussed above).

After sampling five trials, the average error decreased 74% to 0.27 GPMacross the four residential structures and were within 0.11 GPM of themore comprehensive R_(f) data from the previous analysis. This initialresult indicates that it should be possible to generalize calibrationsacross valves in a structure in accord with the second scenario, so thatit is unnecessary to empirically determine flow resistance for eachfixture or appliance in a structure.

Details of Valve/Fixture Event Detection

Exemplary logical steps 200 that are carried out for detection ofvalve/fixture event detection are illustrated in FIG. 11. The outputsignal P(t) from the pressure monitor is input as a signal 202 to both alow pass filter (13 Hz) 204 and a low pass filter (1 Hz) 206. Low passfilter 206 passes the filtered signal to a block 208, which computes thereal Cepstrum of the signal, and to a derivative filter (bandpass) 210,which determines the derivative of the signal. The derivative is inputto a decision block (a comparator) 212, which determines if thederivative is above a first predefined threshold. If so, a gate 216 thatis coupled to receive the derivative from derivative filter 210 isclosed, and the derivative is input to a block 218, which estimates aduration of the event by detecting a time interval between a beginningand an end of the event (determined as explained above). In addition, apositive response from decision block 212 causes a block 214 to find alocal extremum for the derivative signal. The estimated duration of theevent is input to a block 224, which classifies the event as beingeither an open event or a close event. A decision block (comparator) 226determines if the local extremum found in block 214 is above a secondpredefined threshold, and if so, closes a gate 228, which enables a savetemplate block 230 to save a template that includes features for thesignal filtered by low pass filter 204, the real Cepstrum, and thederivative. Each template that is saved for a specific event thusincludes low pressure features, Cepstral features, and derivativefeatures, and the template is identified as being for either a specificvalve open event or a specific valve close event (or some other activityon the system). The saved template is output on a line 232 for storagein a memory (not shown in this Figure).

If the derivative is not above the first predefined threshold indecision block 212, or if the local extremum is not above the secondpredefined threshold in decision block 226, the logic concludes that anevent has not occurred. A block 222 provides for continuing the searchto detect an event by processing the output signal P(t) from thepressure monitor, as discussed above.

In FIG. 12, a flowchart 240 illustrates exemplary steps for classifyingan event by comparison to a plurality of saved templates that werecreated as discussed above in connection with FIG. 11. An unknown typeof event that was detected is input to a preprocessor 242, whichprovides a pressure signal, a Cepstrum signal, and a derivative signalas outputs. The pressure signal is input to a correlative matched filter244 for comparison with low pressure features 246 of a current template,and also supplied to an alignment block 248. The alignment blockprovides compensation for any variation in the main system pressure(i.e., for structures that do not include a pressure regulator), sincechanges in the main system pressure can shift the characteristics for atransient pressure wave sufficiently so that they no longer match thefeatures of the template for that event. Additionally, the alignmentblock may time shift the unknown event to maximally overlap with thesaved templates.

The derivative signal from preprocessor 242 is input to a correlativematched filter block 250 for comparison to Cepstral features 252 of thecurrent template Similarly, the derivative signal is input to acorrelative matched filter 254 for comparison to the derivative features256 of the current template. Correlative matched filter blocks 244, 250,and 254 produce correlation values that are respectively an indicationof how closely the respective low pressure, Cepstral, and derivativefeatures of the transient pressure wave signal for the unknown eventmatch those of each saved template. A high correlation value for each ofthese parameters with the features of a template indicates that there isa high probability that the current unknown event is the event for whichthe template was saved.

The output of correlative matched filter 244 is input a to decisionblock (comparator) 260 to determine if it is above a predefined firstminimum, and if so, a gate 262 is closed, coupling to an enable line.The output from alignment block 248 is applied to a Euclidian distanceblock 256, which determines a Euclidean distance (equal to the squareroot of the sum of the squares of the differences between low pressurefeatures 258 of the current template and the corresponding compensated,aligned features of the pressure signal for the unknown event). TheEuclidean distance result is input to a decision block (comparator) 264to determine if it is below a predefined maximum. If so, a gate 266 isclosed to couple to the downstream side of gate 262. The correlationvalue result from correlative matched filter 250 is input to a decisionstep (comparator) 268 to determine if it is above a predefined secondminimum, and if so, a gate 270 is closed to couple to the downstreamside of gate 266. Finally, the correlation value from correlativematched filter 254 is input to a decision block (comparator) 272 todetermine if it is above a third predefined minimum, and if so, a gate274 is closed, connecting to the downstream side of gate 270. If all ofgates 262, 266, 270, and 274 are closed (corresponding to all inputs toan AND logic being true), the current template is a possible match forthe unknown event being processed, and a block 276 will detect thatcondition to identify the current template as a possible match.

If any one or more of these four gates are open, (while not shownspecifically), the logic will simply proceed to a decision block 278 andnot identify the current template as a possible match to the unknownevent. Decision block 278 determines if any more saved templates remainthat have not yet been compared to the unknown event. If so, a block 280repeats the comparison of the features for the unknown event, with thefeatures of the next saved template, as described above. If no moresaved templates remain, a decision block 282 determines if any eventtemplate was identified as possibly matching the unknown event. If not,the unknown event is classified as a new event in a block 284.Otherwise, an enable signal is supplied to a block 286 to enablechoosing the saved template with the maximum correlation to the featuresof the unknown event. The outputs from correlative matched filter 254feeds into a block 286, where (in response to the enable signal), thesaved template with the highest correlation is chosen as the event type.Outputs from correlative matched filters 244 and 250 can also connect toblock 286 (connections not shown) and be used in place of the outputsfrom correlative matched filter 254. Block 286 uses the outputs from anyone of correlative matched filters 244, 250, or 254, based upon the ROCduring training, as discussed above. The unknown event is thenclassified based on the chosen saved template (i.e., the saved templatewith the highest overall correlation according to outputs fromcorrelative matched filters 244, 250, or 254) in a block 288.

Auto-Calibration

As a way to decrease labor and data entry costs, many water systemutilities are replacing their old water meters with Automatic MeterReading (AMR) systems. An AMR system enables the water system utility toautomatically read their residential/commercial water meters wirelesslythereby greatly reducing costs by eliminating meter readers and manualtranscription errors that occur when the meter is read and when thefield recorded data are input to a billing system to generate the billthat will be provided to the customer. When used in connection with anAMR meter (or any meter capable of transmitting its liquid flowmeasurement data either wirelessly or over a wire lead), the presentnovel system can receive real-time information on aggregate flow volumeand use these aggregate flow data to calibrate the flow estimationalgorithms by determining the flow resistance to some or all of thevalves in the water system of the structure.

To use an AMR (or similar) inline flow volume meter for semi-automaticcalibration to determine flow resistance for different portions of theliquid distribution system, the following steps can be carried out.

-   -   (1) The present novel system queries the water meter (AMR or        other wireless or wire connected inline flow volume meter) to        obtain a baseline accumulated flow volume;    -   (2) A person with access to the structure (e.g., a homeowner for        a residential structure) is instructed to individually open and        then close each valve in the water system of the structure,        leaving the valve in the open state for a short time (e.g., 15        seconds) before closing it;    -   (3) For each valve that is thus actuated by the person, the        present novel system automatically determines that an open/close        event has occurred, as well as the time duration that the valve        was open;    -   (4) The present novel system then queries the water meter after        the close event for each valve, and subtracts the previous flow        volume amount (starting with the baseline value) from the new        flow volume amount to obtain a total amount of flow through the        valve while it was open. This total amount of flow through the        valve is then divided by the valve open duration to obtain flow        rate, which is used as discussed above, to determine the flow        resistance of the water system in regard to the valve that was        just opened and closed.

The semi-automatic calibration can be repeated as desired to compensatefor changes in the water system, e.g., due to changes in temperature ordue to the buildup or corrosion deposits or due to modifications in thelayout of the piping or conduits.

Once the flow resistance of the system for each valve is thus accuratelydetermined, the flow resistance can be used to determine the liquid flowat any valve of the system that is opened.

This entire calibration process can be made completely automatic, bycausing the present novel system to rely on the AMR (or other wirelessor wired connected) meter to provide flow volume data each time that anopen/close event pair is detected. This approach might seem to eliminatethe need for calibration of the water system to determine flowresistance, since it may appear that the present novel system would nolonger be needed to estimate flow—if the flow rate can instead beobtained directly from the AMR meter for every open/close event pair ofa valve. However, using the AMR meter for determining flow haslimitations, because the AMR meter can only provide an aggregate valuefor total water flow. The AMR meter cannot indicate the flow for each oftwo or more overlapping events. In the case of compound events, thepresent novel system would still be used to estimate flow for eachfixture for events that overlap.

Leak Detection

There are two approaches that the present novel system can employ todetect leaks in a water system, depending upon the type and cause of theleak, as follows.

If a high resolution AMR (or other wireless or wire connected) flowvolume meter is installed in the liquid system of a structure, thepresent novel system can detect any low-flow water usage that occursover an extended time interval during which no open valve event isdetected at any fixture in the structure (for example, during a 6-12period during which people are away from the structure, or while peopleare asleep at night, and the water system is not in use or no fixturevalve was opened). Clearly, any aggregate flow volume detected in thewater system by the high resolution flow volume meter during this timewhen no valve has been opened must be an indication of a slow leak. Suchleaks might be due to the formation of a pinhole in a pipe as a resultof freeze damage or due to corrosion, or may result from a continuousleak through a valve that is not completely closed or through a valvethat has a leaky valve stem seal or leaky valve seat.

Another common type of leak is the result of a leaky flapper valve on atoilet, which allows water to flow into a toilet bowl. The excessflowing into the bowl then empties into the drain. This type of leakcauses a periodic toilet open/close event to be detected when the toilettank refills, but differs from a normal toilet flush opening and closingthe toilet flapper valve, since only a portion of the water tank isrefilled when the level in the toilet water tank drops due to flappervalve leakage. When water leaks from a toilet tank through a leakyflapper valve, the level of water in the tank eventually decreases to apoint that automatically triggers the inlet valve for the tank to starta refill. The refill can occur after only 0.1-0.3 gallons have leakedinto the bowl—in contrast to the 2 gallons or more that are emptied fromthe toilet tank into the bowl during a normal toilet flush. The refillthat results from a leaking flapper valve causes an open event while thetoilet inlet valve refills the tank and a close event when the toiletrefill ceases as the water level in the tank reaches the shut off depth,so that the float valve closes. The flapper valve remains nominallyclosed during this refill but still continues leaking water into thebowl.

The open/close event pair for a leaky flapper valve is shorter induration than a normal toilet fill event after a normal toilet flush(since less water is needed to refill the tank), but this shorter termrefill and shorter term valve open event is still detectable by thepresent novel system. In addition to the shorter term of the leak refillevent, the periodic nature of a flapper valve toilet tank leak makes iteasier to differentiate from a normal event (e.g., the toilet mightrefill at 34 minute intervals—if not interrupted by a normal toiletflush). Other types of leaks that exhibit this periodic behavior, suchas leaks from other types of liquid reservoirs having a hysteresisbetween levels used to trigger a valve open and a valve close event canalso be detected using this approach. The characteristics of the waterflow through the valve enable the flapper valve in a specific toiletthat is leaking in this manner to be distinguished when two or moretoilets are included in a structure.

Multiple Sensing Points

Multiple sensors can be installed on a liquid distribution system of astructure to detect pressure transient signatures caused by a singleevent at two or more different locations where the pressure sensors aredisposed. A time-difference between the onset of the two transientsignatures can be used in identifying the event by choosing from a setof known time difference templates that have previously been saved, forexample, generally as disclosed in connection with the saving of eventtemplates that include features for low pressure, derivative, and realCepstrum characteristics of a pressure transient waveform, as discussedherein. The time difference can be used to pinpoint the location of afixture where an event occurred, because of a difference in thedistances between the originating source and each different sensingpoint.

FIG. 13A is a graph 300 illustrating raw pressure output signals 302 and304, which correspond to the same fixture event being detected by twospatially dispersed pressure sensors on a water system. The two outputsignal waveforms from the two different pressure sensors appear similarin shape (although there are some differences in amplitude and highfrequency attenuation due to the different paths followed by thepressure transient waves received by the two pressure sensors anddifferences in the response of the pressure sensors), but the waveformsare offset from each other in time by about 800 ms. This time delayoffset feature is independent of the structure water system pressure andthe amplitude of the waveforms, thus providing a robust approach fordiscriminating the source of an event at the fixture level.

FIG. 13B is a graph 310 showing filtered pressure output signals 312 and314, after they have been filtered through a 13 Hz low pass filter. Thislow pass filter suppresses the higher frequency components of thewaveforms, making the time delay between them more apparent.

Active Water Event Probing

The pressure sensor or transducer used to detect pressure transients inthe water system can be reversed biased to produce a known pressuretransient pulse that propagates through the water system. An exemplaryactive pressure signal wave 322 is shown in a graph 320 in FIG. 14A.This active pressure signal wave is used to interrogate a position ofthe valves in the system by observing reflected signals that arereturned to the pressure sensor. FIG. 14B is a graph 324 illustrating anexemplary reflected pressure signal 326, such as the pressure signalthat is reflected from a closed valve in a water system.

Various characteristics of the reflected pressure signal can be used tocreate a new template that includes characteristic features of thereflected active probe pressure waves. Such templates associated withthe valves in a system can be saved and used for processing subsequentreflected pressure waves from the probe pressure pulse signals suppliedas an output signal from the pressure sensor. Changes in these reflectedsignal features indicate a change in the state of the water system(e.g., a closed valve being opened). An open valve will cause the signalto undergo a high frequency attenuation, as well as a phase shift (seeexemplary reflected signal 326 in FIG. 14B). These two features can beused to estimate the path of the reflected signal and as an indicationof water flow. Active pressure pulse probing may be useful to query thecurrent states of valves in a liquid distribution system of a structure,for example, if events are missed using the novel passive approachdiscussed above.

Exemplary Computing Device for Processing Output Signal from PressureModule

FIG. 15 schematically illustrates an exemplary computing device 350 thatcomprises a computer 364 suitable for implementing the present noveltechnique. Computer 364 may be a generally conventional personalcomputer (PC) such as a laptop, desktop computer, server, or other formof computing device. Computer 364 is coupled to a display 368, which isused for displaying text and graphics to the user, such as data relatedto events, activity, and specific fixtures where water has been or isbeing consumed, as well as the flow rate to specific fixtures. Includedwithin computer 364 is a processor 362. A memory 366 (with both readonly memory (ROM) and random access memory (RAM)), a non-volatilestorage 360 (such as a hard drive or other non-volatile data storagedevice) for storage of data and machine readable and executableinstructions comprising modules and software programs, and digitalsignals, a network interface 352, and an optical drive 358 are coupledto processor 362 through a bus 354. Data that are stored can includetemplates, predefined thresholds, and other parameters used inprocessing the output signal from the pressure module. Any of these datacan alternatively be accessed over a network 370, such as the Internetor other network, through network interface 352. Optical drive 358 canread a compact disk (CD) 356 (or other optical storage media, such as adigital video disk (DVD)) on which machine instructions are stored forimplementing the present novel technique, as well as other softwaremodules and programs that may be run by computer 364. The machineinstructions are loaded into memory 366 before being executed byprocessor 362 to carry out the steps for implementing the presenttechnique, e.g., carrying out divide, multiply, and subtraction steps,as discussed above. The user can provide input to and/or control theprocess through keyboard/mouse 372, which is coupled to computer 364. ABluetooth radio 374 is also connected to bus 354 for receiving aBluetooth radio signal 376 from the pressure module. It will beappreciated that other types of wired or wireless communication linkscan convey the output signal from the pressure module. For example, aWi-Fi radio signal or an Ethernet or uniform serial bus (USB) wiredcommunication link can be used instead of the Bluetooth radio. Theoutput signal can also be stored as data on a non-volatile memory mediumand subsequently processed with computer 364 to review the water usageand/or flow rate in a structure.

Discussion

The novel approach disclosed herein shows significant promise forsingle-point sensing of activity in a liquid distribution system viacontinuous monitoring of the pressure in the system. The approachclearly represents a reliable method for segmenting valve pressureevents from their surrounding pressure sensor output signal stream andfor determining whether a segmented event corresponds to a valve beingopened or closed. Empirical testing has shown the efficacy and accuracyof the present approach. Using data collected in nine residentialstructures, a 95.6% aggregate accuracy in identifying an individualfixture associated with a valve event was demonstrated. Analyzing flowdata collected in four of those residential structures, it has beenshown that an appropriately located and calibrated system can estimatewater usage with error rates comparable to empirical studies oftraditional utility-supplied water meters. The ability to identifyactivity at individual fixtures using a single sensor is itself animportant advance. Adding an additional sensor at a point in the liquiddistribution system separated from a point where the first sensor isinstalled provides additional information about events in the system,based on the delay time between the transient pressure waveform signalsoutput from the sensors. It should again be emphasized that although aninitial evaluation of the present novel approach was applied to monitorwater usage, events, and activities at various fixtures in residentialstructures, there is no reason to limit this approach to either thattype of structure or to monitoring events and activity involving onlywater in a water system. Instead, this approach is applicable to almostany application in which it is desired to monitor events and activityinvolving a liquid flowing through liquid distribution passages such aspipes or conduits. For example, the present approach can be used tomonitor valve events and other activity, to determine flow rates ofvarious liquids, or detect leaks in a chemical processing facility or ina brewery. As noted above, the term “structure” as used herein and inthe claims that follow is intended to be broadly interpreted, so as toencompass any facility in which a liquid distribution system conveys aliquid to various fixtures or through valves or other flow alteringdevices. A water system in a residential structure is thus just oneexample of such a liquid distribution system, and water is just oneexample of such a liquid.

Although the analysis discussed above focused on identifying fixtureevents occurring in isolation, it is clearly important to consider thecase where multiple events overlap. To evaluate the capability of thepresent novel approach in this regard, six compound events werecollected in residential structure H1 (two each of shower/sink,toilet/sink, and shower/toilet/sink overlaps), as partially shown in agraph 190 in FIG. 10, which shows a shower open event 1 overlapping witha toilet open event 2, overlapping with a faucet open event 3, followedby the close event for the faucet, and the close event for the toilet.The event segmentation algorithm was able to correctly segment theseoverlapping events (i.e., was able to identify the end of an ongoingevent when a rapid increase in the magnitude of pressure fluctuationcorresponding to the beginning of an overlapping event was detected). Avisual inspection of the pressure transient waveforms suggests that themagnitude and shape of the events is relatively undisturbed by theoverlap. Further, it should also be possible to classify such compoundevents using the approach described herein.

It was determined that a reliable estimation of flow rate is sensitiveto accurate calibration, and an empirical approach can be used toperform this calibration, as discussed above. It is contemplated thatfurther empirical tests should be able to identify optimal thresholdparameters for the segmentation and identification algorithms discussedabove.

There is initial evidence that water system behavior is generally stableover time, based on a second dataset that was collected in H1 five weeksafter the original collection. The fixture classification methodsexplained above were applied to this pair of datasets using templatesfrom the opposite dataset (classifying unknown events using templatescollected five weeks apart)—without finding any degradation in fixtureidentification performance, which suggests that system behavior might besufficiently stable to apply a variety of machine learning methods toenable auto-calibration of the water system flow rate.

The data collection for the tests at the residential structures includedinstallation of the pressure sensor at several different types offixtures (hose bibs, utility sink faucets, water heater drain valves)with generally good results. Two identical collections of data werecarried out in H9, one using the pressure sensor coupled to a hose biband one using the pressure sensor coupled to a hot water heater drainvalve, under the expectation that performance would be nearly identicalfor each. FIG. 6 reports the performance for the pressure sensor coupledto the hose bib. In contrast, the performance fell to 88.6% for openvalve events and 65.7% for close valve events when the pressure sensorwas moved to the water heater drain valve (only individual fixtureclassification was affected, not segmentation or the determination ofwhether events are open or close events). This change in the performancelevel indicates that the location of the pressure sensor can affect theaccuracy of the results for the present novel approach, but in general,it appears that the coupling of the pressure sensor to almost any otherfaucet bib in a structure other than the drain valve of the water heaterwill provide acceptable performance. There were other examples indicatedin FIG. 6 where the current novel approach differed in its ability toidentify the specific fixture associated with valve open and closeevents. Although valve open and close events come in pairs, the presentapproach classifies them individually. It is thus contemplated that byjointly classifying pairs of valve open and valve close events, improvedaccuracy can be achieved in identifying the fixture where the valves arelocated Similarly, although as discussed above, flow rate is estimatedindependently of fixture identification, the two are clearly related andit is expected that improved results might be achieved in estimatingflow rate in connection with the fixture consuming water.

Although the concepts disclosed herein have been described in connectionwith the preferred form of practicing them and modifications thereto,those of ordinary skill in the art will understand that many othermodifications can be made thereto within the scope of the claims thatfollow. Accordingly, it is not intended that the scope of these conceptsin any way be limited by the above description, but instead bedetermined entirely by reference to the claims that follow.

What is claimed is:
 1. A method comprising: monitoring water pressure atone or more locations of a water distribution system of a building usingone or more pressure sensors to produce one or more pressure signals,the building comprising two or more water appliances coupled to thewater distribution system; and using the one or more pressure signals todetermine that a first water appliance of the two or more waterappliances is consuming water and to determine a first individual waterusage amount for the first water appliance.
 2. The method of claim 1,further comprising: using the one or more pressure signals to determinethat a second water appliance of the two or more water appliances isconsuming water and to determine a second individual water usage amountfor the second water appliance.
 3. The method of claim 2, furthercomprising: determining the first individual water usage amount and thesecond individual water usage amount when the first and second waterappliances are consuming water simultaneously; and determining the firstindividual water usage amount and the second individual water usageamount when the first and second water appliances are consuming waterseparately.
 4. The method of claim 2, further comprising: communicatingthe first individual water usage amount and the second individual waterusage amount to a user via a graphical user interface.
 5. The method ofclaim 1, further comprising: using the one or more pressure signals todetermine a total water usage amount for the water distribution system.6. The method of claim 1, further comprising: using the one or morepressure signals to detect a leak in the water distribution system. 7.The method of claim 1, further comprising: receiving flow volumeinformation from a flow volume meter that measures an aggregate value oftotal water flow for the water distribution system; and using the flowvolume information to calibrate using the one or more pressure signalsto determine individual water usage amounts of the two or more waterappliances.
 8. The method of claim 1, wherein: the one or more locationsof the water distribution system are not located in the waterdistribution system between a water source and the first waterappliance.
 9. The method of claim 1, wherein: the one or more locationsof the water distribution system are located at one or more end pointsof the water distribution system.
 10. The method of claim 1, wherein:the two or more water appliances comprise at least two of: a faucet, abathroom sink, a toilet, a kitchen sink, a bathtub, a dishwasher, ashower, a hot water heater, or a clothing washing machine.
 11. A systemcomprising: one or more pressure sensors configured to monitor waterpressure at one or more locations of a water distribution system of abuilding to produce one or more pressure signals, the buildingcomprising two or more water appliances coupled to the waterdistribution system; and a processing system configured to use the oneor more pressure signals to determine that a first water appliance ofthe two or more water appliances is consuming water and to determine afirst individual water usage amount for the first water appliance. 12.The system of claim 11, wherein: the processing system is furtherconfigured to use the one or more pressure signals to determine that asecond water appliance of the two or more water appliances is consumingwater and to determine a second individual water usage amount for thesecond water appliance.
 13. The system of claim 12, wherein: theprocessing system is further configured to: determine the firstindividual water usage amount and the second individual water usageamount when the first and second water appliances are consuming watersimultaneously; and determine the first individual water usage amountand the second individual water usage amount when the first and secondwater appliances are consuming water separately.
 14. The system of claim12, wherein: the processing system is further configured to communicatethe first individual water usage amount and the second individual waterusage amount to a user via a graphical user interface.
 15. The system ofclaim 11, wherein: the processing system is further configured to usethe one or more pressure signals to determine a total water usage amountfor the water distribution system.
 16. The system of claim 11, wherein:the processing system is further configured to use the one or morepressure signals to detect a leak in the water distribution system. 17.The system of claim 11, wherein: the processing system is furtherconfigured to: receive flow volume information from a flow volume meterthat measures an aggregate value of total water flow for the waterdistribution system; and use the flow volume information to calibratethe processing system for using the one or more pressure signals todetermine individual water usage amounts of the two or more waterappliances.
 18. The system of claim 11, wherein: the one or morelocations of the water distribution system are not located in the waterdistribution system between a water source and the first waterappliance.
 19. The system of claim 11, wherein: the one or morelocations of the water distribution system are located at one or moreend points of the water distribution system.
 20. The system of claim 11,wherein: the two or more water appliances comprise at least two of: afaucet, a bathroom sink, a toilet, a kitchen sink, a bathtub, adishwasher, a shower, a hot water heater, or a clothing washing machine.